mlsanityData to 14 Oct 2026

Resilient Learning-Based Control Under Denial-of-Service Attacks

arXiv 2409.07766 ↗First posted 12 Sep 2024eess.SY cs.SYcited by 0

On the map: Deep reinforcement learning methods → Safe and constrained RL

Abstract (arXiv)

In this paper, we have proposed a resilient reinforcement learning method for discrete-time linear systems with unknown parameters, under denial-of-service (DoS) attacks. The proposed method is based on policy iteration that learns the optimal controller from input-state data amidst DoS attacks. We achieve an upper bound for the DoS duration to ensure closed-loop stability. The resilience of the closed-loop system, when subjected to DoS attacks with the learned controller and an internal model, has been thoroughly examined. The effectiveness of the proposed methodology is demonstrated on an inverted pendulum on a cart.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Safe and constrained RL, one of 15 directions in Deep reinforcement learning methods. Builds on 2 of its 32 references that are in the map. First posted on arXiv 12 Sep 2024.

Builds on

2 of 32 references.

References · 2

  1. 2021Data-Driven Resilient Predictive Control under Denial-of-Servicecited by 4
  2. 2019Robust Learning-based Predictive Control for Discrete-time Nonlinear Systems with Unknown Dynamics and State Constraintscited by 3

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2024Resilient control under denial-of-service and uncertainty: An adaptive dynamic programming approach ↗
  2. 2026Model-Free Reinforcement Learning Control for Resilient Cyber-Physical Systems
  3. 2026Inverting Self-Triggered Control: Adversarial Reinforcement Learning for Sparse Denial-of-Service Attacks
  4. 2025Action Robust Reinforcement Learning via Optimal Adversary Aware Policy Optimization
  5. 2026On the Comparison of Reinforcement Learning and Adaptive Control for Linear Systems under Packet Loss and Uncertainty
  6. 2025AdaDoS: Adaptive DoS Attack via Deep Adversarial Reinforcement Learning in SDN
  7. 2024Beyond Worst-case Attacks: Robust RL with Adaptive Defense via Non-dominated Policies
  8. 2025ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning